Visual Manual Research Articles

Self-regulation of drivers’ mobile phone use: The influence of driving context

Mobile phone use while driving is a major concern for traffic safety. Various studies indicate negative effects of distracted driving and recent Naturalistic Driving studies report substantial increase in crash risk of mobile phone use while driving. The increasing level of vehicle automation is likely to further increase phone use behind the wheel, as the automation takes over part of the driving task drivers are likely to experience boredom and feel more confident to get involved in other activities such as phone use. This may further increase the distraction related accidents on our roads. However, the extent to which this further increases depends largely on the drivers’ level of self-regulation of risk, the timing of engagement in phone activities in relation to the driving context.The objective of this study was to investigate if drivers self-regulate their mobile phone use, specifically focussed on the visual manual (VM) task which is associated with the largest increase in risk, while driving in relation to different driving contexts. For this study naturalistic driving data was analysed of Dutch car drivers collected in the UDRIVE project. The results show that Dutch drivers spent over 9% of all driving time engaging in mobile phone related tasks, including calling and VM tasks such as texting. Drivers used their mobile phone significantly less when a passenger was present. Also, significantly more VM tasks were initiated during standstill than for the other speed categories. In addition, on rural roads relatively less time was spent on VM tasks and on highways relatively more time was spent on VM tasks. Overall the results show that the driving context seems to influence the initiation and involvement in VM tasks, which is an indication that drivers self-regulate this behaviour.

Investigating Effect of Driver-, Vehicle-, and Road-Related Factors on Location-Specific Crashes with Naturalistic Driving Data

According to NHTSA, traffic accidents cost the United States billions of U.S. dollars each year. Intersection accidents alone accounted for 23% of the 32,675 motor crash deaths in 2014. With the advent of the largest naturalistic driving data set in the United States collected by the SHRP2 Naturalistic Driving Study project, this study performs a crash-only analysis to identify driver-, vehicle-, and roadway-related factors that affect the driving risk at different location types using a machine learning tool. The study then analyzes the most important factors obtained from the machine learning analysis to identify how they affect crash risk. The results, in order of importance of variables, were driver behavior, locality, lane occupied, alignment, and through travel lanes. Also, drivers who violated traffic signals were four times more likely to be involved in a crash than drivers who did not. Those who violated stop signs were two times more likely to be involved in crashes than those who did not. Drivers performing visual-manual (VM) tasks at uncontrolled intersections were 2.7 times more likely to be involved in crashes than those who did not engage in these tasks. At nonintersections, drivers who performed VM tasks were 3.4 times more likely to be involved in crashes than drivers who did not. These findings add to the evidence that the establishment of safety awareness programs geared toward intersection safety is imperative.

The relative impact of smartwatch and smartphone use while driving on workload, attention, and driving performance

The impact of using a smartwatch to initiate phone calls on driver workload, attention, and performance was compared to smartphone visual-manual (VM) and auditory-vocal (AV) interfaces. In a driving simulator, 36 participants placed calls using each method. While task time and number of glances were greater for AV calling on the smartwatch vs. smartphone, remote detection task (R-DRT) responsiveness, mean single glance duration, percentage of long duration off-road glances, total off-road glance time, and percent time looking off-road were similar; the later metrics were all significantly higher for the VM interface vs. AV methods. Heart rate and skin conductance were higher during phone calling tasks than “just driving”, but did not consistently differentiate calling method. Participants exhibited more erratic driving behavior (lane position and major steering wheel reversals) for smartphone VM calling compared to both AV methods. Workload ratings were lower for AV calling on both devices vs. VM calling.

How conceptual overlap and modality pairings affect task-switching and mixing costs.

Manipulating the pairings of stimulus and response modalities has been shown to affect how response selection processes for distinct tasks interact. For example, Stephan and Koch (Psychol Res 75(6):491-498, 2011) found smaller performance costs when participants switched between visual-manual (VM) and auditory-vocal (AV) tasks (modality compatible; MC) compared to between visual-vocal (VV) and auditory-manual (AM) tasks (modality incompatible; MI). However, in the Stephan and Koch study, there was conceptual overlap between one set of stimuli and one set of responses. For the MC pair, these stimuli and responses belonged to the same task, whereas for the MI pair, they belonged to different tasks. To examine how conceptual overlap affected switch and mixing costs, we conducted two experiments. Experiment 1a was a near replication of Stephan and Koch in which conceptual overlap was present in the MC AV task. In contrast, Experiment 1b reduced conceptual overlap within the MC AV task and increased it in the MI VV task. In Experiment 1a, we replicated Stephan and Koch's findings: larger switch costs were observed for the MI pair; in Experiment 1b, we found numerically greater switch costs in the MC condition. In Experiment 2, we reduced conceptual overlap in both tasks and found no effect of modality compatibility on switch costs. However, mixing costs were primarily driven by modality compatibility, regardless of conceptual overlap. These results highlight the different roles that conceptual overlap and modality pairings have on switch and mixing costs.

A meta-analysis of in-vehicle and nomadic voice-recognition system interaction and driving performance

Driver distraction is a growing and pervasive issue that requires multiple solutions. Voice-recognition (V-R) systems may decrease the visual-manual (V-M) demands of a wide range of in-vehicle system and smartphone interactions. However, the degree that V-R systems integrated into vehicles or available in mobile phone applications affect driver distraction is incompletely understood. A comprehensive meta-analysis of experimental studies was conducted to address this knowledge gap. To meet study inclusion criteria, drivers had to interact with a V-R system while driving and doing everyday V-R tasks such as dialing, initiating a call, texting, emailing, destination entry or music selection. Coded dependent variables included detection, reaction time, lateral position, speed and headway. Comparisons of V-R systems with baseline driving and/or a V-M condition were also coded. Of 817 identified citations, 43 studies involving 2000 drivers and 183 effect sizes (r) were analyzed in the meta-analysis. Compared to baseline, driving while interacting with a V-R system is associated with increases in reaction time and lane positioning, and decreases in detection. When V-M systems were compared to V-R systems, drivers had slightly better performance with the latter system on reaction time, lane positioning and headway. Although V-R systems have some driving performance advantages over V-M systems, they have a distraction cost relative to driving without any system at all. The pattern of results indicates that V-R systems impose moderate distraction costs on driving. In addition, drivers minimally engage in compensatory performance adjustments such as reducing speed and increasing headway while using V-R systems. Implications of the results for theory, design guidelines and future research are discussed.

Comparing the Relative Impact of Smartwatch and Smartphone Use While Driving on Workload, Attention, and Driving Performance

A simulator study evaluated the extent to which the use of a smartwatch to initiate phone calls while driving impacts driver workload, attention, and performance, relative to visual-manual (VM) and auditory-vocal (AV) calling methods on a smartphone. Participants completed four calling tasks using each method while driving in a simulator and completing a remote detection response task (R-DRT). Among the 36 participants evaluated, R-DRT miss rates and reaction time were comparable between AV calling on the smartwatch and smartphone, but significantly higher using VM calling on the smartphone. Participants also exhibited more erratic driving behavior (lane position deviation and major steering wheel reversals) with smartphone VM calling compared to both AV calling methods. Finally, participants rated AV calling on both devices as entailing lower workload than VM calling on the smartphone. Overall, few differences emerged for the metrics reported between voice calling on a smartphone versus a smartwatch while driving.

Examination of drivers' cell phone use behavior at intersections by using naturalistic driving data

IntroductionMany driving simulator studies have shown that cell phone use while driving greatly degraded driving performance. In terms of safety analysis, many factors including drivers, vehicles, and driving situations need to be considered. Controlled or simulated studies cannot always account for the full effects of these factors, especially situational factors such as road condition, traffic density, and weather and lighting conditions. Naturalistic driving by its nature provides a natural and realistic way to examine drivers' behaviors and associated factors for cell phone use while driving. MethodIn this study, driving speed while using a cell phone (conversation or visual/manual tasks) was compared to two baselines (baseline 1: normal driving condition, which only excludes driving while using a cell phone, baseline 2: driving-only condition, which excludes all types of secondary tasks) when traversing an intersection. ResultsThe outcomes showed that drivers drove slower when using a cell for both conversation and visual/manual (VM) tasks compared to baseline conditions. With regard to cell phone conversations, drivers were more likely to drive faster during the day time compared to night time driving and drive slower under moderate traffic compared to under sparse traffic situations. With regard to VM tasks, there was a significant interaction between traffic and cell phone use conditions. The maximum speed with VM tasks was significantly lower than that with baseline conditions under sparse traffic conditions. In contrast, the maximum speed with VM tasks was slightly higher than that with baseline driving under dense traffic situations. Practical applicationsThis suggests that drivers might self-regulate their behavior based on the driving situations and demand for secondary tasks, which could provide insights on driver distraction guidelines. With the rapid development of in-vehicle technology, the findings in this research could lead the improvement of human-machine interface (HMI) design as well.

Practice-related reduction of dual-task costs under conditions of a manual-pedal response combination

Severe dual-task costs emerge when two tasks are performed at the same time. Schumacher, Seymour, Glass, Kieras, and Meyer (2001) showed a complete reduction of dual-task costs after extensive dual-task practice with a visual-manual (VM) task and an auditory-verbal (AV) task. First, we replicated these findings and found task conditions sufficient to achieve a high level of dual-task cost reduction (Experiment 1). Using these conditions, we tested whether the Schumacher et al. findings generalise to a different dual-task situation, in which participants practised a VM task and an auditory-pedal (AP) task (VM-AP) conjointly (Experiment 2). In the VM-AP task situation we found reduced dual-task costs after practice. Dual-task costs, however, remained on a high level after eight sessions of practice and also when extending practice to 12 sessions. No single participant showed evidence for time sharing in the VM-AP dual task. These results suggest that the finding of complete dual-task cost reduction does not generalise to the VM-AP task combination used in the present study. We discuss different factors potentially relevant for the observation of persisting dual-task costs over practice in the VM-AP task.